1. Field of the Invention
Embodiments of the invention described herein pertain to the field of robots. More particularly, but not by way of limitation, embodiments of the invention enable an agricultural robot system and method of robotic harvesting, pruning, culling, weeding, measuring and managing of agricultural crops.
2. Description of the Related Art
The use of robots to automate tasks performed by people is increasing. Robots provide several important benefits over human labor including improved efficiency, less expense, more consistent and higher quality work performed, and the ability to perform hazardous work without endangering people. Individually and collectively, these benefits help businesses increase margins and profits, which is essential for maintaining competitiveness.
Agriculture is one industry with traditionally low profit margins and high manual labor costs. In particular, harvesting can be expensive. For some crops, such as tree fruit, harvesting labor represents the growers' single largest expense, up to 50% of total crop cost. Increasing labor costs and labor shortages threaten the economic viability of many farms. Therefore, replacing manual labor with robots would be extremely beneficial for harvesting. Additional benefits could be obtained through automating other tasks currently done manually such as pruning, culling, thinning, spraying, weeding, measuring and managing of agricultural crops.
GPS controlled automated tractors and combines already operate in wheat and other grain fields. Automated harvesters exist that can blindly harvest fruit by causing the fruit to drop from a plant into a collection device. For example, Korvan Industries, Inc. makes equipment than shakes oranges, grapes, raspberries, blueberries, etc. off plants. These harvesting approaches have wide scale applicability, but are not applicable to the harvesting of all crops.
For example, while oranges may be harvested en mass by shaking the tree, this method only works for the fruit that will be processed. Shaking cannot be used for picking oranges sold as fresh, i.e. table fruit. The violent nature of this harvesting technique can bruise the fruit and tear the skin, which is both unappealing to the consumer and causes the fruit to rot quickly.
Thus, whole tree harvesting approaches comprising “shaking,” are inappropriate for picking fresh fruits and vegetables such as apples, pears, tomatoes and cucumbers that are to be sold as whole fruit. A different approach is required, one in which each piece of fruit is picked individually.
People have attempted to develop mechanical pickers to pick whole fruits for years. For example, Pellenc, a French manufacturer, built a prototype orange picker, but abandoned the project. One common failure mode for these picking systems was that they could not locate fruit located on the inside of the tree that cannot be seen from outside the canopy. To date, no equipment exists that can pick fresh fruits and vegetables efficiently enough to compete with human labor in cost or yield. Furthermore, machines have been used in an attempt to hedge grape vines. Hedging grape vines provides a rough cut to the vines that blindly shapes the vines. The final pruning of the canes on the grape vines is non-trivial and is best performed using a holistic view of the grape vine and planning before pruning is attempted. To date, no known machines are configured to intelligently perform the final pruning of grape vines. Known final pruning methods utilize humans operating pruning devices by hand. In addition, there are no known systems that scout and pre-plan harvesting, pruning, culling or other agricultural functions. Similarly to harvesting and pruning, automating other tasks such as thinning, spraying, culling, weeding, measuring and managing of agricultural crops can lower costs and increase consistency and quality.
A farmer's main inventory is the crop in the field. Managing that inventory requires knowledge about that inventory such as the count, size, color, etc. of the crop on each tree, bush, or vine. To date, farmers estimate these parameters from relatively small samples taken by manual observation that are prone to errors when projecting parameters of the entire crop. Because of the time, cost, and effort required to do these estimates, farmers often do not even perform these estimates. Satellite imagery has recently enabled macro-level estimates of some of these crop parameters such as tree vigor, crop ripeness by color, or the presence of certain diseases. While this is useful information, it does not provide data at the individual tree/bush/vine level. For at least the reasons detailed in this section, there is a need for an agricultural robot system and method.